ABSTRACT.- The incidence of type 1 diabetes (T1D) has quadrupled over the past 40 years, now affecting1.4 million people in the U.S. At least as many asymptomatic persons express multiple islet autoantibodies and will develop T1D in the next 10 years. The Diabetes Autoimmunity in the Young (DAISY) prospective cohort is now entering adulthood so we propose to explore the natural history of autoimmune diabetes phenotypes in young adults. While it is generally accepted that the chronic autoimmune destruction of pancreatic ?-cells leading to T1D is triggered by an interaction of environmental factor(s) with a relatively common genetic background, the specific cause remains elusive. We also poorly understand the determinants of prolonged relapsing-remitting course of islet autoimmunity (IA) leading to T1D. We propose to shift the paradigm from evaluating a handful of candidate triggers at a time, to predictive modeling across an array of biomedical domains and serial measurements spanning up to 25 years of life. Our focus is to expand our understanding of changes in human proteome associated with development of IA and T1D. Combining environmental exposures and immune marker data with genetic, epigenetic, transcriptomic, and metabolomic profiles will allow us to interrogate the complex interplay of IA/T1D biomarkers utilizing Bayesian integration with the power of individualized algorithms to inform new approaches to prevention. 1) The established cohort of youth at high risk of T1D and other autoimmune diseases (n=1149, median age 17.2 y, IQR 13.5-20.2 y) will be followed to estimate overall burden of T1D, celiac, thyroid and other autoimmune disease, by age 25. We will further explore the apparent heterogeneity of islet autoimmunity and its implications for adult-onset diabetes. 2) Targeted proteomics will validate candidate protein biomarkers of IA and T1D reported from discovery studies. 3) Integrative Bayesian modeling, based on a small set of disparate features (gene variants, proteins, or metabolites) will be used to generate individualized prediction algorithms in IA progressors vs. non-progressors and to identify potential pathways. The proposed studies are important to reach our overarching goals: to find the environmental causes of T1D, develop primary prevention, and inform public health screening for several autoimmune disorders in children and adolescents. We will continue to make every effort to share DAISY resources with multiple investigators studying T1D and other autoimmune diseases through an open-source database/ repository.